Systematic approach to estimate non-uniform heat generation rate in heat transfer problems using liquid crystal thermography and inverse methodology

References

• J. Zueco, F. Alhama, and C. G. Fernandez, “Inverse determination of heat generation sources in two-dimensional homogeneous solids: application to orthotropic medium,” International Communications in Heat and Mass Transfer, vol. 33, no. 1, pp.49–55, 2006.DOI: 10.1016/j.icheatmasstransfer.2005.08.013.
   Web of Science ®Google Scholar
• K. Erhart, E. Divo and A. Kassab, “An inverse localized meshless technique for the determination of non-linear heat generation rates in living tissue,” in: 9th AIAA/ASME Joint Thermophysics and Heat Transfer Conference, 3587, 2008
   Google Scholar
• C.-H. Huang and H.-C. Lo, “A three-dimensional inverse problem in estimating the internal heat flux of housing for high speed motors,” Applied Thermal Engineering, vol. 26, no. 14–15, pp.1515–1529, 2006.DOI: 10.1016/j.applthermaleng.2005.12.009.
   Web of Science ®Google Scholar
• C.-H. Huang and C.-Y. Huang, “An inverse problem in estimating simultaneously the effective thermal conductivity and volumetric heat capacity of biological tissue,” Applied Mathematical Modelling, vol. 31, no. 9, pp.1785–1797, 2007.DOI: 10.1016/j.apm.2006.06.002.
   Web of Science ®Google Scholar
• S. Parthasarathy and C. Balaji, “Estimation of parameters in multi-mode heat transfer problems using bayesian inference–Effect of noise and a priori,” International Journal of Heat and Mass Transfer, vol. 51, no. 9–10, pp.2313–2334, 2008.DOI: 10.1016/j.ijheatmasstransfer.2007.08.031.
   Web of Science ®Google Scholar
• F.-B. Liu, “A modified genetic algorithm for solving the inverse heat transfer problem of estimating plan heat source,” International Journal of Heat and Mass Transfer, vol. 51, no. 15–16, pp.3745–3752, 2008.DOI: 10.1016/j.ijheatmasstransfer.2008.01.002.
   Web of Science ®Google Scholar
• O. Cortés, G. Urquiza, and J. A. Hernández,“Inverse heat transfer using Levenberg-Marquardt and particle swarm optimization methods for heat source estimation,“ in Applied Mechanics and Materials, Urriolagoitia-Calderón, G., Hernández-Gómez, L. H, Toledo-Velázquez, M., (Eds.), Switzerland: Trans Tech Publ, 2009, 15, 35–40.
   Google Scholar
• C. Balaji and T. Padhi, “A new ANN driven MCMC method for multi-parameter estimation in two-dimensional conduction with heat generation,” International Journal of Heat and Mass Transfer, vol. 53, no. 23–24, pp.5440–5455, 2010.DOI: 10.1016/j.ijheatmasstransfer.2010.05.064.
   Web of Science ®Google Scholar
• C.-H. Huang and W.-L. Chang 2010, “An inverse problem in estimating the volumetric heat generation for a three-dimensional encapsulated chip,” Journal of Electronic Packaging, 132 (1), 9.
   Web of Science ®Google Scholar
• N. Gnanasekaran and C. Balaji, “An inexpensive technique to simultaneously determine total emissivity and natural convection heat transfer coefficient from transient experiments,” Experimental Heat Transfer, vol. 23, no. 3, pp.235–258, 2010.DOI: 10.1080/08916150903564788.
   Web of Science ®Google Scholar
• -C.-C. Wang and C.-Y. Yang, “Inverse method in simultaneously estimate internal heat generation and root temperature of the T-shaped fin,” International Communications in Heat and Mass Transfer, vol. 37, no. 9, pp.1312–1320, 2010.DOI: 10.1016/j.icheatmasstransfer.2010.07.004.
   Web of Science ®Google Scholar
• V. M. Luchesi and R. T. Coelho, “An inverse method to estimate the moving heat source in machining process,” Applied Thermal Engineering, vol. 45, pp. 64–78, 2012.DOI: 10.1016/j.applthermaleng.2012.04.014.
   Web of Science ®Google Scholar
• P. F. de Sousa, V. L. Borges, I. C. Pereira, M. B. da Silva, and G. Guimarães, “Estimation of heat flux and temperature field during drilling process using dynamic observers based on Green’s function,” Applied Thermal Engineering, vol. 48, pp. 144–154, 2012.DOI: 10.1016/j.applthermaleng.2012.04.061.
   Web of Science ®Google Scholar
• D. C. Knupp, C. P. Naveira-Cotta, H. R. Orlande, and R. M. Cotta, “Experimental identification of thermophysical properties in heterogeneous materials with integral transformation of temperature measurements from infrared thermography,” Experimental Heat Transfer, vol. 26, no. 1, pp.1–25, 2013.DOI: 10.1080/08916152.2011.631079.
   Web of Science ®Google Scholar
• K. Das, R. Singh, and S. C. Mishra, “Numerical analysis for determination of the presence of a tumor and estimation of its size and location in a tissue,” Journal of Thermal Biology, vol. 38, no. 1, pp.32–40, 2013.DOI: 10.1016/j.jtherbio.2012.10.003.
   PubMed Web of Science ®Google Scholar
• K. Das and S. C. Mishra, “Non-invasive estimation of size and location of a tumor in a human breast using a curve fitting technique,” International Communications in Heat and Mass Transfer, vol. 56, pp. 63–70, 2014.DOI: 10.1016/j.icheatmasstransfer.2014.04.015.
   Web of Science ®Google Scholar
• T. Hotta, C. Balaji, and S. Venkateshan, “Experiment driven ANN-GA based technique for optimal distribution of discrete heat sources under mixed convection,” Experimental Heat Transfer, vol. 28, no. 3, pp.298–315, 2015.DOI: 10.1080/08916152.2013.871867.
   Web of Science ®Google Scholar
• E. Saniei, S. Setayeshi, M. E. Akbari, and M. Navid, “Parameter estimation of breast tumour using dynamic neural network from thermal pattern,” Journal of Advanced Research, vol. 7, no. 6, pp.1045–1055, 2016.DOI: 10.1016/j.jare.2016.05.005.
   PubMed Web of Science ®Google Scholar
• R. Das and B. Kundu, “Prediction of heat generation in a porous fin from surface temperature,” Journal of Thermophysics and Heat Transfer, vol. 31, no. 4, pp.781–790, 2017.DOI: 10.2514/1.T5098.
   Web of Science ®Google Scholar
• R. Hatwar and C. Herman, “Inverse method for quantitative characterisation of breast tumours from surface temperature data,” International Journal of Hyperthermia, vol. 33, no. 7, pp.741–757, 2017.DOI: 10.1080/02656736.2017.1306758.
   PubMed Web of Science ®Google Scholar
• M. Bahador, M. M. Keshtkar, and A. Zariee, “Numerical and experimental investigation on the breast cancer tumour parameters by inverse heat transfer method using genetic algorithm and image processing,” Sādhanā, vol. 43, no. 9, pp.1–10, 2018.DOI: 10.1007/s12046-018-0900-4.
   Web of Science ®Google Scholar
• H. Kumar, S. Kumar, N. Gnanasekaran, and C. Balaji, “A Markov chain monte Carlo-Metropolis Hastings approach for the simultaneous estimation of heat generation and heat transfer coefficient from a teflon cylinder,” Heat Transfer Engineering, vol. 39, no. 4, pp.339–352, 2018.DOI: 10.1080/01457632.2017.1305823.
   Web of Science ®Google Scholar
• S. Kumar, P. S. Jakkareddy, and C. Balaji, “A novel method to detect hot spots and estimate strengths of discrete heat sources using liquid crystal thermography,” International Journal of Thermal Sciences, vol. 154, pp. 106377, 2020.DOI: 10.1016/j.ijthermalsci.2020.106377.
   Web of Science ®Google Scholar
• S. C. Godi, S. Abraham, A. Pattamatta, and C. Balaji, “Evaluation of candidate strategies for the estimation of local heat transfer coefficient from wall jets,” Experimental Heat Transfer, vol. 33, no. 1, pp.40–63, 2020.DOI: 10.1080/08916152.2019.1570983.
   Web of Science ®Google Scholar
• N. B. D. Do, et al., “Thermal management of an interventional medical device with double layer encapsulation,” Experimental Heat Transfer, pp. 1–18, 2021.DOI: 10.1080/08916152.2021.1946208.
   Google Scholar
• A. Berber, M. Gürdal, and K. Bağrsakç, “Prediction of heat transfer in a circular tube with aluminum and Cr-Ni alloy pins using artificial neural network,” Experimental Heat Transfer, vol. 34, no. 6, pp.547–563, 2021.DOI: 10.1080/08916152.2020.1793826.
   Web of Science ®Google Scholar
• V. Mathew and T. K. Hotta, “Experimental investigation of substrate board orientation effect on the optimal distribution of IC chips under forced convection,” Experimental Heat Transfer, vol. 34, no. 6, pp.564–585, 2021.DOI: 10.1080/08916152.2020.1793827.
   Web of Science ®Google Scholar
• M. Behle, K. Schulz, W. Leiner, and M. Fiebig, “Color-based image processing to measure local temperature distributions by wide-band liquid crystal thermography,” Applied Scientific Research, vol. 56, no. 2–3, pp.113–143, 1996.DOI: 10.1007/BF02249377.
   Google Scholar
• N. Abdullah, A. R. A. Talib, A. A. Jaafar, M. A. M. Salleh, and W. T. Chong, “The basics and issues of thermochromic liquid crystal calibrations,” Experimental Thermal and Fluid Science, vol. 34, no. 8, pp.1089–1121, 2010.DOI: 10.1016/j.expthermflusci.2010.03.011.
   Web of Science ®Google Scholar
• G. Grewal, M. Bharara, J. Cobb, V. Dubey, and D. Claremont, “A novel approach to thermochromic liquid crystal calibration using neural networks,“ San Francisco, California, 2006, pp. 1918–1924, 17, DOI:10.1088/0957-0233/17/7/033.
   Google Scholar
• MATLAB (R2019a), The MathWorks Inc, U.S.A., URL www.mathworks.com/products/matlab, 2019 .
   Google Scholar
• P. S. Jakkareddy and C. Balaji, “A non-intrusive technique to determine the spatially varying heat transfer coefficients in a flat plate with flush mounted heat sources,” International Journal of Thermal Sciences, vol. 131, pp. 144–159, 2018.DOI: 10.1016/j.ijthermalsci.2018.03.009.
   Web of Science ®Google Scholar
• G. K. Marri and C. Balaji, “Liquid crystal thermography based study on melting dynamics and the effect of mushy zone constant in numerical modeling of melting of a phase change material,” International Journal of Thermal Sciences, vol. 171, pp. 107176, 2022.DOI: 10.1016/j.ijthermalsci.2021.107176.
   Web of Science ®Google Scholar
• S. P. Venkateshan, Mechanical Measurements, Ane Books. New Delhi: Springer, 2009.
   Google Scholar
• COMSOL, Multiphysics V.5.4., COMSOL AB., Stockholm, Sweden, URL www.comsol.com., 2018 .
   Google Scholar